Electric bus networks are steadily gaining ground as the prominent option for urban public transport. However, in contrast to conventional transit systems operated by diesel buses, electric bus networks are particularly vulnerable with respect to energy supply, both in terms of power level availability and the unobstructed access to charging points. Indeed, power fluctuations can prevent buses from adequately recharging at designated points, affecting extended areas of operation. Similarly, queue formation at terminal stops can lead to poor schedule adherence and excessive delays. In this context, this study addresses research gaps by presenting a realistic and flexible design framework for fully electric public transport networks, tackling both the route network and the charging infrastructure design. To handle the uncertainty associated with power supply, Robust Optimization (RO) is employed for solving the charging infrastructure location problem while maintaining computational tractability. Queuing delays due to charging are also modeled and minimized. To address the integrated design of route networks and wireless chargers, RO is coupled with Multi-Objective Particle Swarm Optimization within a bi-level methodological framework. Different scenarios for power supply variability are considered. Results show that depending on policy priority, the cost of robustness significantly changes.

电动公交车网络正在稳步发展成为城市公共交通的重要选择。然而，与柴油公交车运营的传统交通系统相比，电动公交车网络在能源供应方面特别脆弱，无论是在功率水平可用性还是无障碍接入充电点方面。事实上，电力波动会阻止公交车在指定地点充分充电，从而影响扩展的运营区域。类似地，在终点站排队会导致不遵守时间表和过度延误。在此背景下，本研究通过提出一个现实且灵活的全电动公共交通网络设计框架来解决研究空白，解决路线网络和充电基础设施设计。为了处理与电源相关的不确定性，采用稳健优化 (RO) 来解决充电基础设施位置问题，同时保持计算的易处理性。由于充电导致的排队延迟也被建模并最小化。为了解决路由网络和无线充电器的集成设计问题，RO 在双层方法框架内与多目标粒子群优化相结合。考虑了电源可变性的不同场景。结果表明，根据政策优先级，稳健性成本显着变化。为了解决路由网络和无线充电器的集成设计问题，RO 在双层方法框架内与多目标粒子群优化相结合。考虑了电源可变性的不同场景。结果表明，根据政策优先级，稳健性成本显着变化。为了解决路由网络和无线充电器的集成设计问题，RO 在双层方法框架内与多目标粒子群优化相结合。考虑了电源可变性的不同场景。结果表明，根据政策优先级，稳健性成本显着变化。